Instructor:
This course will be taught by David Banks, Professor of the Practice of Statistics, Duke University & Director, SAMSI

Course Outline:
This course is being offered in conjunction with the SAMSI semester-long research program on Deep Learning. The course will start with a review of standard neural networks, and then progress to modern deep learning, including convolutional neural networks, recursive neural networks, generative adversarial networks, and various kinds of autoencoders. We shall discuss training strategies, architecture search, regularization and quantization.

There will be mathematics in the course, and a degree of mathematical sophistication is expected from the students, but the material will all be self-contained. The emphasis will be upon heuristics and applications. There will be projects and presentations at the end of the semester, and students will work on those in small groups. Each group will need to have at least one member who can program in Python or a comparable language.

Instructor:
This course will be taught by David Banks, Professor of the Practice of Statistics, Duke University & Director, SAMSI

Course Outline:
This course is being offered in conjunction with the SAMSI semester-long research program on Deep Learning. The course will start with a review of standard neural networks, and then progress to modern deep learning, including convolutional neural networks, recursive neural networks, generative adversarial networks, and various kinds of autoencoders. We shall discuss training strategies, architecture search, regularization and quantization.

There will be mathematics in the course, and a degree of mathematical sophistication is expected from the students, but the material will all be self-contained. The emphasis will be upon heuristics and applications. There will be projects and presentations at the end of the semester, and students will work on those in small groups. Each group will need to have at least one member who can program in Python or a comparable language.

Instructor:
This course will be taught by David Banks, Professor of the Practice of Statistics, Duke University & Director, SAMSI

Course Outline:
This course is being offered in conjunction with the SAMSI semester-long research program on Deep Learning. The course will start with a review of standard neural networks, and then progress to modern deep learning, including convolutional neural networks, recursive neural networks, generative adversarial networks, and various kinds of autoencoders. We shall discuss training strategies, architecture search, regularization and quantization.

There will be mathematics in the course, and a degree of mathematical sophistication is expected from the students, but the material will all be self-contained. The emphasis will be upon heuristics and applications. There will be projects and presentations at the end of the semester, and students will work on those in small groups. Each group will need to have at least one member who can program in Python or a comparable language.

Description:
Modeled after similar events occurring in New York and Boston, the goal is to bring together researchers and applied scientists working in all different areas of machine learning, including industrial applications, academic theory, and everything in between, for a day of technical talks and posters.

The program will include a poster session, and anyone wishing to contribute a poster must submit an abstract; not all abstracts will be selected as posters, depending on space limitations. The due date for poster submissions is August 29, 2019. Poster selections will be announced on September 5th.

Heavy snacks will be provided for all participants, lunch and breakfast are on your own. There are a large number of restaurants at the neighboring Brodhead Center.

Instructor:
This course will be taught by David Banks, Professor of the Practice of Statistics, Duke University & Director, SAMSI

Course Outline:
This course is being offered in conjunction with the SAMSI semester-long research program on Deep Learning. The course will start with a review of standard neural networks, and then progress to modern deep learning, including convolutional neural networks, recursive neural networks, generative adversarial networks, and various kinds of autoencoders. We shall discuss training strategies, architecture search, regularization and quantization.

There will be mathematics in the course, and a degree of mathematical sophistication is expected from the students, but the material will all be self-contained. The emphasis will be upon heuristics and applications. There will be projects and presentations at the end of the semester, and students will work on those in small groups. Each group will need to have at least one member who can program in Python or a comparable language.

Instructor:
This course will be taught by David Banks, Professor of the Practice of Statistics, Duke University & Director, SAMSI

Course Outline:
This course is being offered in conjunction with the SAMSI semester-long research program on Deep Learning. The course will start with a review of standard neural networks, and then progress to modern deep learning, including convolutional neural networks, recursive neural networks, generative adversarial networks, and various kinds of autoencoders. We shall discuss training strategies, architecture search, regularization and quantization.

There will be mathematics in the course, and a degree of mathematical sophistication is expected from the students, but the material will all be self-contained. The emphasis will be upon heuristics and applications. There will be projects and presentations at the end of the semester, and students will work on those in small groups. Each group will need to have at least one member who can program in Python or a comparable language.